Recently, the predictor-estimator framework [1]} is proposed to evaluate the translation quality without the need of word alignments.
It consists of a word prediction RNN trained on parallel corpora that extracts representative features from the source sentence and its translation, and a neural quality estimation model that makes predictions based on the extracted features.
[2]} [2]} proposes to use a bidirectional transformer language model to produce the joint representation and designs features that measure the degree of mis-matching.
Pre-trained language models such as BERT [4]}, are also introduced into the predictor-estimator framework as the feature extractor.
